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    Area of Science:

    • Control Theory
    • Optimization
    • Distributed Systems

    Background:

    • Multiagent systems often face complex optimization problems with individual constraints.
    • Cooperative search for a global optimum requires efficient distributed algorithms.

    Purpose of the Study:

    • To develop a distributed optimization algorithm for second-order discrete-time multiagent systems.
    • To address cooperative optimization within the intersection of constrained sets, with private data access for each agent.

    Main Methods:

    • A projection-based distributed optimization algorithm using auxiliary dynamics is proposed, avoiding direct gradient sharing.
    • Convergence criteria for step size and algorithm parameters are derived for strongly convex objective functions.

    Main Results:

    • The algorithm achieves convergence to a unique optimal solution for strongly convex local objective functions.
    • The optimization result is extended to convex local objective functions with appropriate parameter selection.

    Conclusions:

    • The developed algorithm offers an effective approach for distributed optimization in constrained multiagent systems.
    • The findings are validated through practical and numerical examples, demonstrating the algorithm's efficacy.